Development of an Optimal Vehicle-to-Grid Aggregator for Frequency Regulation

By Sekyung Han, S. Han, Kaoru Sezaki. IEEE Transactions on Smart Grid, 2010

1,104 citations recorded by the source corpus.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/3abb3b596181cebceca240160e7d3f47ae5621c0

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Citations in the corpus

Coordinated Charging of Electric Vehicles for Congestion Prevention in the Distribution Grid (2014, neutral): “2) Similar methods: • Implement centralized control/marketing method to maximize business values [1]–[7]; • Optimize the charging process of EVs [6], [8]–[11].”
Autonomous Distributed V2G (Vehicle-to-Grid) Satisfying Scheduled Charging (2012, background): “Aggregated V2G pool consisted by huge EVs contributes greatly to the supply and demand dispatch, and each EV user may obtain the incentive cost [8], [9].”
A Survey on the Electrification of Transportation in a Smart Grid Environment (2012, background): “[46] proposed an aggregator for V2G frequency regulation that makes it more efficient.”
Coordinated Charging of Plug-In Hybrid Electric Vehicles to Minimize Distribution System Losses (2011, methodology): “The second is that these objectives can be easily integrated as loss constraints in other PHEV charging objective functions such as those in [12], [13] which minimize system operating costs to, or maximize profits to, an aggregator.”
Electric Vehicle Charging on Residential Distribution Systems: Impacts and Mitigations (2015, background): “In [45] and [46], the optimal charging control for EVs is achieved by optimizing the energy usage of the distributed EVs for V2G frequency regulation services.”
A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms (2015, background): “This is mainly due to the inability to pump the energy stored in the batteries back to the grid, and the lower overall performance of the unidirectional option, compared to the bidirectional solution [83], [178].”
A Distributed Demand Response Algorithm and Its Application to PHEV Charging in Smart Grids (2012, methodology): “Compared to previous work such as [14], [15] and [13] where there is often a centralized control entity that computes optimal solutions using techniques such as dynamic programming, our algorithm is fully distributed and can scale to a large number of PHEVs.”
Smart Grid — The New and Improved Power Grid: A Survey (2012, neutral): “Coordinated charging h as hence been proposed to mitigate these negative impacts usin g s ochastic programming [48, 49], quadratic optimization [ 93], particle swarm optimization [218], and dynamic programmin g [95].”
Optimal Scheduling of Vehicle-to-Grid Energy and Ancillary Services (2012, methodology): “This formulation considers the selling of bulk energy and spinning reserves which were not considered in [3], [7], and [17].”
Optimal Scheduling for Charging and Discharging of Electric Vehicles (2012, background): “The paper in [16] develop ed an aggregator for V2G frequency regulation with the optimal control strategy, which aims to maximize the revenue.”
Optimal Charging Strategies for Unidirectional Vehicle-to-Grid (2011, background): “Because there is only unidirectional power flow, the periods of charging cannot be decoupled from the periods performing regulation as in [10].”
Real-Time Coordination of Plug-In Electric Vehicle Charging in Smart Grids to Minimize Power Losses and Improve Voltage Profile (2011, methodology): “Reference [24] uses dynamic programming to make efficient use of the distributed power of electric vehicles”